Statistical Analysis of Features for Detecting Leukemia

Diabetes is a major chronic syndrome caused by a series of metabolic abnormalities in which blood glucose levels are abnormally high for an indeterminate amount of time. It influences various organs in the human body, resulting in a variety of complex diseases such as stroke, renal disease, pulmonary embolism, eyesight, and so on. Diabetes Disorders (DD) are presently one of the healthcare top causes of mortality. Predictive analytics in the health care system is a huge obstacle, but if accurate early prediction is achieved, the potential risk and degree of diabetes may be significantly decreased. Machine learning (ML) techniques are now used to analyze medical datasets at an earlier stage of life in keeping people safe. In this research, we utilized several ML approaches notably Logistic Regression, Decision Tree (DT), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) on PIMA Indian Diabetes Dataset in order to monitor and evaluate their performances in diabetes prediction. The performance of the various ML algorithms employed in this research suggests which algorithm is most suitable in diabetes prediction. It is observed that among all the models XGBoost had outperformed the other ML techniques with an accuracy of 80.73% while SVM was the second-best performing model with a classification accuracy of 80.21%. Thus, employing ML techniques, this study aims to assist doctors as well as clinicians in the early detection of diabetes.

In the current landscape of abundant high-dimensional datasets, addressing classification challenges is pivotal. While prior studies have effectively utilized Backward Elimination (BE) for disease detection, there is a notable absence of research demonstrating the method's significance through comprehensive comparisons across diverse databases. The study aims to extend its contribution by applying BE across multiple machine learning algorithms (MLAs)Nave Bayes (NB), k-Nearest Neighbors (KNN), and Support Vector Machine (SVM)on datasets associated with preventable diseases (i.e. heart failure (HF), breast cancer (BC), and diabetes). This study aims to elucidate and recommend significant differences observed in the application of BE across diverse datasets and machine learning (ML) methods. This study conducted testing on four distinct datasetsraisin, HF, BC, and early-stage diabetes risk prediction datasets. Each dataset underwent evaluation with three MLAs: NB, KNN, and SVM. The application of BE successfully eliminated non-significant attributes, retaining only influential ones in the model. In addition, t-test results revealed a significant impact on accuracy across all datasets (p-value < 0.05). In specific algorithmic evaluations, SVM exhibited the highest accuracy for the raisin dataset at 87.22%. Additionally, KNN attained the utmost accuracy in the heart failure dataset with an accuracy of 86.31%. In the breast cancer dataset, KNN again excelled, achieving an accuracy of 83.56%. For the diabetes dataset, KNN proved the most accurate, reaching 96.15%. These results underscore the efficacy of BE in enhancing the execution of MLAs for disease detection.

In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.

Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children’s bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929.

The classification of autism spectrum disorder by machine learning methods on multiple datasets for four age groups

Cardiovascular diseases (CVDs) have become increasingly prevalent and severe worldwide, necessitating a transition to more advanced and precise predictive models. Traditional approaches to the prognosis of coronary heart disease often lead to higher morbidity and mortality due to delayed early detection and inadequate individualized analysis. Machine learning (ML) techniques offer a groundbreaking solution, capable of analyzing vast amounts of patient data to provide early and accurate predictions of cardiac diseases. This shift holds great promise across various domains, including heart imaging, cardiothoracic surgery, cardiovascular anesthesiology, coronary critical care, pediatric cardiovascular disease, predictive cardiology, and interventional cardiology. In this comprehensive review, we evaluate several ML techniques, specifically focusing on classification and prediction algorithms, to assess their effectiveness in predicting cardiovascular diseases. We examine a range of algorithms such as K-Nearest Neighbors (K-NN), Decision Trees (DT), Support Vector Machines (SVM), Random Forests, Logistic Regression, XGBoost, Extra Trees Classifier, Gaussian Naive Bayes, and Artificial Neural Networks. By comparing these models, we aim to identify the most reliable and effective predictive tools for cardiovascular diseases. Notably, algorithms like Decision Trees and Extra Trees Classifiers have demonstrated remarkable performance, with recorded accuracies of 93.19% and 98.15%, respectively. This study underscores the importance of implementing machine learning approaches to enhance the accuracy and predictive capabilities in cardiac care. By leveraging these models, we can significantly improve early diagnosis, leading to better patient outcomes across cardiology specialties.

Stroke is a global health problem and one of the leading causes of adult disability. Early detection and prompt treatment are needed to minimize further damage to the affected brain area and complications to other parts of the body. Machine learning techniques can be used to predict stroke detection. Machine learning algorithms such as Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree are compared in this study to obtain the best performance in predicting stroke. The implementation stages in this research consist of the pre-processing data, the application of the algorithm and the evaluation and analysis. The Naïve Bayes algorithm obtains better Accuracy, Precision, Recall, and F1-Measure values compared to other algorithms. The values of Accuracy, Precision, Recall, and F1-Measure obtained by Naïve Bayes are 93.93%, 88.23%, 93.93%, and 91.00%, respectively. So the conclusion of this study is that the Naïve Bayes algorithm has the best performance compared to the SVM, KNN and Decision Tree algorithms in predicting stroke.Keywords: decision tree, klasifikasi, k-nearest neighbor, naïve bayes, stroke, support vector machine

Blood circulation in the body is very crucial for life. So, one of the most important parts of our body is the heart since it circulates blood to every part of our body. Any damage to the heart can be life-threatening so, we need a dependable and precise error-free system for the proper treatment using machine learning techniques and big data. There are several machine learning techniques that can be used to predict heart-related disease at an early stage to stop it from becoming fatal. Some of the machine learning techniques with higher accuracy are SVM (support vector machine), naïve Bayes, decision tree, KNN (K-nearest neighbor). Big data technologies are extremely important for heart disease prediction. The high cost, complex study of modern medicines, and limited data available of a small population of patients create problems in improving quality care and research. The big data approach gathers this kind of large number of health records and data of wide variety, high volume, and utmost acquisition speed. By combining these large datasets researchers aim to build algorithms for better accuracy and prediction of heart-related disease. So in this research, a comparative study is performed for heart diseases using large dataset using various machine learning techniques like SVM, artificial neural network, naïve Bayes, and KNN to find the best suitable technique for early heart disease prediction.

Orthopedic patients have been increasing in hospital because of road traffic accidents, advanced age, a lack of exercise, inadequate nutrition, and other factors. The suggested article uses Machine Learning (ML) techniques to examine the patient reports. The ability to mimic the human actions is called ML. It is a subclass of AI that solves a number of healthcare-related issues. Here ML algorithms are used for health-related data. It solves a number of healthcare-related issues. ML is the process of a machine imitating intelligent human activities. It belongs to the Artificial Intelligence (AI) subclass. ML algorithms are used for medical data such as Logistic Regression, Support vector machine, K-Nearest Neighbor, Random Forest, Decision Tree, Artificial Neural Network to predict orthopedic illnesses such as Normal, Hernia and Spondylolisthesis orthopedic. ML techniques have increased the speed and accuracy for diagnosis. The most serious and urgent cases require rapid care. It improves patient care by lowering human error and stress on medical staff. Our primary objective is to improve machine performance and decrease incorrect categorization.

One of the most serious diseases affecting people is heart disease, which also contributes significantly to the increasing death rate. Recently, Machine Learning (ML) methods have been utilized to detect and identify several types of heart disease, these methods can assist in enhancing the accuracy of heart disease classification. In this research, total ten machine learning (ML) models that’s are used, the first ML technique is Random Forest (rf) second ML technique is Decision Tree (dt), third is Support Vector Machine (svm) fourth ML techniques is K-Nearest Neighbors (knn), fifth ML technique is Logistics Regression (lr) and respectively Extra Trees Classifier (et), Gradient Boosting Classifier (gbc), Naive Bayes (nb), Ridge Classifier (ridge) and tenth ML technique is Linear Discriminant Analysis (ldr) are implemented. In addition, Recursive Feature Elimination (RFE) feature assortment method is used to find the significant features and enhance the efficiency of detecting heart disease using two text-based datasets. Finally, a detailed comparison will be implemented to presented that after smearing the feature selection method to recognize the most efficient model for detecting heart disease and then specify its severity. Some parameters, including accurateness, exactness, recollection, and F1-score, are offered to comparation the efficiency of each classifier. The actual outcomes of this research that improved the classification accurateness.

Data security is regarded to be one of the crucial challenges in this fast-growing internet world. Data generated through internet is exposed to various types of vulnerabilities and exploits. Security mechanisms such as Intrusion Detection System (IDS) are designed to detect various types of vulnerabilities and attacks. Various Machine Learning (ML) and Deep Learning (DL) techniques are used for building IDS. In this paper, we aim to build Deep Neural Network (DNN)-based IDS for attack detection and classification. DNN technique has certain challenges such as complex network structure, co-adaptation of feature vectors, over-fitting, to name a few. We aim to address these challenges by using AntiRectifier layer and variants of dropout namely, Standard dropout, Gaussian dropout, and Gaussian Noise. In this paper, we have evaluated DNN-based IDS using NSL-KDD, UNSW_NB-15 and CIC-IDS-2017 dataset. The experimental results show that DNN-based IDS with AntiRectifier layer outperforms compared to ML techniques such as Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbours (k-NN) and variants of dropout namely, Standard dropout, Gaussian dropout, and Gaussian noise in terms of accuracy, precision, recall, f-score, and false positive rate.

Given the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would be helpful. However, due to the specific characteristics of the field of healthcare, a suitable DM and ML methodology adapted to these particularities is required. The applied methodology must structure the different stages needed for data-driven healthcare, from the acquisition of raw data to decision-making by clinicians, considering the specific requirements of this field. In this paper, we focus on a case study of cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases, which is important for example in insurance-related investigations. By analyzing in detail this case study in a real scenario, we show how taking care of those particularities enables the generation of reliable predictive models in the field of healthcare. Using a database of 302 samples, we have generated several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms. The results show that it is possible to reliably predict the presence of cervical pain (accuracy, precision, and recall above 90%). We expect that the procedure proposed to apply ML techniques in the field of healthcare will help technologists, researchers, and clinicians to create more objective systems that provide support to objectify the diagnosis, improve test treatment efficacy, and save resources.

Identified mainly by memory loss and social inability, dementia may result from several different diseases. In the world with ever growing elderly population, the problem of dementia is rising. Despite being one of the prevalent mental health conditions in the community, it is not timely identified, reported and even understood completely. With the massive improvement in the computational power, researchers have developed machine learning (ML) techniques to diagnose and detect neurodegenerative diseases. This current work reports a comparative study of performance of several ML techniques, including support vector machine, logistic regression, artificial neural network, Naive Bayes, decision tree, random forest and K-nearest neighbor, when they are employed in identifying dementia from clinical datasets. It has been found that support vector machine and random forest perform better on datasets coming from open access repositories such as open access series of imaging studies, Alzheimer’s disease neuroimaging initiative and dementia bank datasets.

The advancement of technology contributes in the development of many field of life. One of the major fields to focus on is the field of higher education. Actually, Saudi's universities provide free education to the students, so large number of students apply to the universities. In response to that, universities usually maintain admission policies. Universities' admission policies and procedures focus on students Grade Point Average in high school (GPAH), General Aptitude Test (GAT) and Achievement Test (AT). In fact, guiding students to the suitable major improves students' achievements and success. This paper studies the admission criteria for universities in Saudi Arabia. This paper investigates the hidden details that lies behind students' GP AH, GAT and AT. Those details influence the process of students' major selection at universities. Indeed, this research uses machine learning models to include more features such as the grades of high school courses to predict the suitable majors for the students. We use K-Nearest Neighbor (KNN), Decision Tree (DT) and Support Vector Machine (SVM) to classify students into suitable majors. This process enhances the enrollments of applicants in appropriate majors. Furthermore, the experiments show that KNN gives the highest accuracy rate as it reaches 100%, while DT's accuracy rate is 81 % and SVM's accuracy rate is 75%.

Applying data mining techniques to classify patients with suspected hepatitis C virus infection